Author Archive for Daniel Huffman

Today’s effort comes to my attention via a reader, Nicholas, who wrote to me early last year and suggested that I have a look at the following piece:

Click to enlarge

It was created by Chris Dickersin-Prokopp, who posted it on his blog and on Greater Greater Washington (where it got most of its comments) in February 2013. If you read the comments, you can see that, unfortunately, the response was not quite as positive he had hoped for. Not only did Mr. Dickersin-Prokopp accept the criticism gracefully, but he said he hoped that perhaps he’d find his way onto the pages of this very blog. So, let’s have a look.

To me, the real issue here is not in the map design itself, but instead the disconnect between the author and his readers. It’s an issue in any sort of creative field: you make something, and it turns out that the people who view that something were looking for something else. Mr. Dickersin-Prokopp’s detractors seemed to be looking for details on the map:

“Wow. I find it hard to think of a worse way to visualize these data. How am I supposed to draw any conclusions from a picture showing thousands of overlapping circles?”

“I can’t tell how many overlapping circle I’m looking at in different areas. Why not just make each sale a dot and color code the dots for different sales price ranges?”

But the author, I speculate, was trying to paint a broad picture: where, generally, home sales are more concentrated and more expensive. Counting individual circles isn’t necessary here. You just need to be able to tell where there are roughly more or roughly less. I think that his map does this job just fine, giving you the 10-second quick understanding of the basic pattern. His readers, however, were looking for something else: a map that let them break these patterns down in more detail, and count the number of houses in neighborhoods (everyone always wants to know what’s going on in their neighborhood/city/state when they see a map). The map isn’t ready to do that, as is, and the second quote above suggests a reasonable alternative. But one that, probably, wouldn’t have done as good a job at telling the quick broad story.

Two different ways of reading the same set of data: the big pattern, and the individual location data. The author had one in mind, but some of his commenters had another. What to do? Well, there may well have been a good symbology to satisfy both. But, setting that aside, how do we get people on the same page as us if we’re making maps? I think this map could use a little more context, to steer people’s minds toward reading it as the author wishes. Annotations would be handy—highlight some region or other and say, “over here, we can see a dense area of inexpensive home purchases that happened because of Reason X.” Get people thinking about the patterns and pull them away from the details that the author is less focused on.

The other option is to just change the map in response to comments, trying to give the audience what they’re looking for. It’s tough, as a creator, to have to give up on part of your vision and preferences, but it’s necessary if you want anyone to look at your creation. If no one likes it, then it doesn’t matter how great you think it is: no one is looking. Of course the hard part about that is that while many don’t agree with your choices, they don’t agree with each other, either. Some folks liked Mr. Dickersin-Prokopp’s effort:

“The message the map intends to convey is clear and obvious.”

If he changes things, he may displease some folks who were previously happy. There’s not much to be done about that, except to accept that you can’t please everyone.

Bigger picture aside, there are some other interesting things to dissect here, starting with the legend. which states, “Circle size represents sale price. One square meter equals one dollar.” If I interpret this correctly, this means that, in order to divine the sale price of a home you must determine how large a circle when compared against the map scale. That is, you pretend the circle is a real area of land in DC, measure the size of that land, and then convert to dollars. This is quite a fascinatingly weird way of explaining the symbology, and I’ve never seen anything like it. For good reason: it’s difficult enough so as to be largely useless. If you want people to understand what symbols mean, show the symbols; a legend should show more than it should tell. It would be better if this particular map showed people some sample circles and the values they represent. Then readers can reasonably eyeball things.

Typical proportional symbol legend designs. Via Esri Mapping Center.

It’s highly unlikely that anyone is planning on measuring the exact price of each home (and if they do, they deserve what they get). We just need to let them get a rough idea of the pattern, and the circles above do that easily, without requiring people to visually measure square meters.

Speaking of which, that brings us to the scale. The increments could be a lot more helpful. At the very least, they should be in kilometers, but miles would be likely much more intelligible to an US audience. And the scale bar probably doesn’t need to be as wide as DC itself. No one is likely to do any measurements with it. To that end, I’d argue it could be dropped altogether. It is an unnecessary map element.

One Nice Thing: Putting your work out there for criticism and comment is tough, especially if you’re opening it up to the harsh gaze of the random Internet visitor. Mr. Dickersin-Prokopp handled the experience gracefully, and listened to the feedback he received, where I’ve seen other people in similar situations try to rationalize away criticism, or strike back angrily. Kudos to him.

Things have been fairly quiet around here lately, and I apologize for that. I’ve had some maps in mind to write about, but much of my spare time has been taken up by a major project. I present to you, the NACIS Atlas of Design:

Click to visit the Atlas of Design website

This is a book I edited along with the superbly awesome Tim Wallace. It’s a refereed collection of some of the world’s best cartography. You may recall my announcement earlier this year that we were taking entries. Well, we had over 140 of them, and then a panel of judges selected 27 finalists to be published in this anthology.

This book is very important to me personally. In this era of quick and easy mapping, I feel that all too often we are focused only on the coding, or the data, and not enough on how the whole thing looks, and how it makes readers feel. This is a book about how maps look, and why we need to remember that beautiful and clever design is an essential ingredient in mapmaking. We wanted to produce a volume to honor talented people, and to inspire everyone out there toward new understandings of the role of aesthetics and design in mapmaking. I hope you’ll enjoy it, and I very much hope it will give you something to ponder.

I must apologize to you for an error which I committed in my most recent writing here on Cartastrophe.

A few days ago I posted an analysis of two maps from the PBS series America Revealed. In developing my critique, I relied on a piece from the Daily Mail in the UK, which posted images of many of the maps in the series. I wrote up my post based solely on what the Daily Mail said about the maps, rather than going back and looking at them in their original context. I had originally thought that the PBS series hadn’t yet aired, and so I didn’t look too hard. But, in fact, it was available online, having appeared in the US about two months ago, and had I spent about 15 seconds looking online I would have learned this fact immediately.

Having now reviewed the maps as they appeared on the PBS series, I find that my critique of the first map was based on some erroneous assumptions about the map’s subject. It is not a map of raw job losses, but a map of manufacturing jobs in the 1990s, which then (as the video proceeds), shows the blue dots winking out as jobs vanish over time. If you go back to the original post, you’ll see that I’ve added some notes about the new context information.

I would like to apologize to you, readers, and to the creators of those maps, for judging them outside of their appropriate context. It is my responsibility to research the works that I show here, to ensure that the comments I make are based on a correct assessment of how the map is used, and what it purports to be about. I take this responsibility seriously, and I try to limit my critique to things that are positively known about the map, rather than assumptions. I did not do my homework sufficiently in this case.

It is my opinion that pretty much all of my critique remains valid in light of the new information on these maps, so hopefully I dodged a bullet there. However, that’s ultimately for you to judge. It’s up to you, as my readers, to determine if I am making fair and reasonable points about the maps I examine. I provide links to their sources for this purpose. Because of my laxness, neither of us had all the information needed to assess these works.

This morning, a friend emailed me a link to this story at the Daily Mail, which contains a number of beautiful maps on America’s infrastructure networks. Go check it out; I’ll wait here while you spend fifteen minutes going “ooh!” and “ahh!” at all the images.

While they are beautiful, they are not without problems. Lovely visuals, deep conceptual errors. Let’s start with the one that stuck out to me, the visualization of job losses. At least, I assume it’s job losses. The caption at the Daily Mail reads, “Unemployment: The number of job losses in the U.S. chronicled in this stunning image.” Unemployment is different than job losses, so I’m not sure which is being mapped, but I suspect the latter.(EDIT: I’ve lately found the video that these maps come from. This is actually a map showing the distribution of manufacturing jobs in the 1990s. In the video, the dots wink out — turn black — to show the decline of the manufacturing sector. Seen at about 12 min, 50 sec in the video here: http://www.pbs.org/america-revealed/episode/4/)

A dot density map is approximately the worst way to look at a data set like this. The author(s) took the number of jobs lost in each state, converted that to a certain number of dots, and then scattered those dots all throughout the state. The result is misleading in several ways. First off, this is really a map of job loss density. The brightest areas are where the dots are clustered, which means a lot of jobs were lost in a tight space. Texas lost a lot of jobs, probably as many as Ohio or Illinois (I’m not going to count every dot, but they probably have similar numbers). But the latter two states look worse off, because they have about the same number of dots crammed into a smaller physical area. Actually, it’s not even a map of job loss density, because the areas of the states are distorted due to curvature of the Earth in this view. Washington, for example, is a lot smaller than if we were looking at it from directly above, and its unemployment picture therefore looks worse. So, this isn’t really a map of “job losses” so much as it’s a map of “job losses divided by the size of each state, if you distort the sizes of the states a lot.”

So, reading the dot concentrations will simply mislead us. But what about the number of dots? If we painstakingly counted them, we could certainly find out at least how many jobs were lost in a state without having to worry about this density issue. Well, the second big problem is that these data aren’t normalized to population. There are a lot more dots in Illinois than in Wyoming. This is because Illinois is a very populous state, whereas no one lives in Wyoming. No account was taken, seemingly, of population differences. Some states are being hit harder by the recession than others, but all you can tell from this map is that places that had more people lost more jobs. I quickly found this table prepared last year and it points out that Nevada has been hammered by the downturn, losing 8% of its jobs. Ohio, on the other hand, lost only 2.6%. But when you look at the map, which state looks worse off?

Lastly, jobs weren’t lost evenly throughout each state, so why scatter the dots evenly? Probably because the author(s) only had state-based data, but making some account for population locations would be nice. Why show a sea of lost jobs in eastern California, which is mostly desert, mountains, and unpopulated forests? The exact locations of the dots are meaningless anyway, since they’ve been distributed randomly in each state.

This map should have been a choropleth of job losses per state divided by population. It’s not nearly as sexy, but it’s also not seriously misleading.

(EDIT: As said, this critique was based on an inaccurate description of what this map is about, and I apologize for not doing my research. Much of the critique still stands — showing dots still seems odd for this data set. It’s a map of “manufacturing jobs divided by the size of each state, if you distort the sizes of the states.” The need for normalization is probably less, as well, though it couldn’t hurt.)

The dot density map was what galled me. On the other hand, my friend Chris, who sent me the link, was bothered by the map of wireless access towers:

I can’t say for certain without hearing the accompanying narration (EDIT — see below), but the author(s) very likely received a data set which had point locations for towers and broadcast power, and simply made circles proportional to the power. That’s all quite reasonable, but the map very much looks like it’s trying to portray the actual signal coverage areas, and that’s a very different thing. Most any electromagnetic signal coming from a tower is not going to move in a perfect, even circle away from the transmission point. It gets distorted by a lot of things — buildings get in the way, so do mountains and other terrain features, and the Earth’s magnetic field also affects it. And if it’s been broadcast by a directional antenna, the signal starts out stronger in some directions than others. Presenting these transmissions as circles is an overly idealized view of how they work. Even if the authors were only using circles as a symbol for power, rather than suggesting these are areas of coverage, a lot of people are going to misunderstand this as the latter. Every reader brings their own interpretations to the map, and it’s not always the one the author wants. The best you can do is try and head them off.

(EDIT: Again, I’ve now looked up the relevant section in the video to figure out what they map is actually of. It’s found at 40:20 in this video: http://www.pbs.org/america-revealed/episode/4/ — the narrator doesn’t actually say what the map is of; it just shows while he talks about how your wireless signal is bounced between towers, so I’d say the critique above stands as is. If anything, I’d say my point about misunderstanding the map is even stronger, because the program really does leave it up to you to figure out what’s going on.).

All the maps on this site have a pseudo-realistic appearance, and they’re even discussed as though you’re “seeing what the nation looks like from the skies.” My colleague Marty Elmer pointed out to me that this realism means an increased expectation of accuracy. If you’re telling me that I’m floating above the US, really seeing the job losses or broadcast signals, I’m likely to believe that this is really how things are distributed on the ground. That the signals really are circles, or the jobs really were lost in the woods of northern Michigan. I’m less likely to take the thematic data as an abstraction because the base map, with its fancy lighting effects and clouds, doesn’t look very abstracted. Generalization is not just about redrawing your linework to the right scale; it’s about credulity. This was one of the biggest points I would emphasize to my students back when I lectured on the subject. Visual abstraction needs to match data abstraction. Readers seeing a highly simplified visualization will assume the data are likewise telling a highly simplified story. If they see a very realistic and detailed basemap, they’re likely to assume that the data have been treated similarly.

In the end, we’re left with beautiful, but potentially (or sometimes outright) misleading images, and that’s a travesty. These are for a television special, and besides going in front of millions of eyes on PBS, these works will very likely go around the Internet to millions more. The maps are lovely to look at, and this means they’ll get a chance to misinform many, many more people. It’s a shame, because it’s a squandered opportunity to inform people about actual facts. Imagine pairing quality visuals with well-thought-out data treatment and map concepts, and how far that would go. But infotainment isn’t about the substance, just the style. See the headline to that Daily Mail link, for example — “Secret corpse flights,” as though this transport of bodies were illicit, rather than a routine movement of your loved ones to their desired resting place. It makes a good story, even if it’s not true.

One Nice Thing: I could go on for a long time about how excellent these look. Maps need to be beautiful if you want people to look at them and spend the time to learn something off them. I’d love to see the author(s) continue to do this great work, just with better data and ideas for portraying it.

Today’s maps come to my attention via my colleague Sam Matthews, whom I hope to get to contribute to this blog someday. He alerted me to mapsofworld.com and the wealth of intriguing and often unfortunate cartographic specimens to be found there. They have lots of material worth discussing, but for now, I’m just going to pick out a couple to highlight a problem I’ve not talked about before. Let’s start with their map of world mineral resources.

via mapsofworld.com. Click to visit.

Fairly innocuous-looking, to be sure. Tan land, blue water. Standard stuff. But if you look carefully, and you obsess about projections as I do, you’ll see that this map not only has blue water, but that it’s sitting on a blue background. That is to say, there is no distinction between the map and the background it’s drawn on. The color used on this map to mean “water” is also used for areas that are not a map. Here’s a hastily annotated copy to help explain:

A lot of people looking at this map are going to think that there’s a bunch of extra water on the planet that simply doesn’t exist. The Bering Strait between Alaska and Russia is only about 50 miles wide. Here, it looks like a huge expanse hundreds of miles across. And this map isn’t the worst of them. Here’s another one from the same site:

via mapsofworld.com. Click to visit site.

This map has an entire extra ocean added at the top, a vast unnamed and unexplored expanse beyond the Arctic Ocean, somehow more north than the North Pole itself. It’s bizarre and unnecessary, and worse, it’s misleading. If you want to know why Americans have such poor knowledge of world geography, at least a fraction of the answer lies in the above, along with all of the other carelessly assembled maps that people end up learning from.

Both of these maps could be fixed by simply inserting a neatline. A neatline is a border, usually just a black line, that separates the map from the rest of the page. The lines I have drawn in my annotated examples above are neatlines, albeit approximated. In cases like this, neatlines are the difference between “map sitting on a blue background” and “map of an alternate dimension where there are extra oceans.”

I’m actually not a fan of neatlines — I think they’re frequently unnecessary, as I argue in a post on my other blog today. While these maps would be improved by adding a neatline, they could skip it entirely by just making the page background something other than the color of the water. A bold concept, but I’m willing to promote it. Imagine: a map with blue water and a white background.

I'm going to patent this!

This phantom ocean problem issue crops up a lot with maps made using projections that aren’t rectangular, like the venerable Robinson projection above, or the Winkel Tripel. These maps have curved edges, and I suppose that bothers people who want maps to fit inside rectangles. Maybe I’m missing something. Maybe they ran a focus group and found out that people hate non-rectangular maps, or that they cause seizures or something, and that we ought to add a few extra seas here and there to fill it out.

Like most maps featured here, I can’t entirely fathom what goes on in the mapmaker’s head that makes them think it’s alright to just make up some extra water. The slogan for mapsofworld.com is “We do magic to Maps.” Maybe this is what they mean.

Critical geographers, I’m sure, could have a field day with what such maps say about people. The idea here seems to be that the landforms on the map are data, and that the oceans are merely filler, no better than the background. And it’s true, we’re a pretty land-centered species, for obvious reasons. Bodies of water are often second-class citizens on many maps, thought of only as “not-land,” or “no data.” And phantom oceans, like the above, are probably the result.

A few of you probably saw this post over on somethingaboutmaps, but it’s important enough that I want to repeat it here.

Today I’d like to give a little publicity to a couple of new projects I’m involved in, and which need help from people like you. Both of these are organized through NACIS, the North American Cartographic Information Society.

Atlas of Design

First off, NACIS is creating a new publication, the Atlas of Design, which is intended to be a showcase for top-notch cartographic work around the world. We need help from you, though, to make it happen. If you know of some great work out there, let us know at atlas@nacis.org. It doesn’t have to be something you’ve made — if you’ve seen a great map out there that someone else has made, encourage them to submit to us, or let us know and we’ll get in touch. We want work out there that gets to the heart of great cartography and makes us think about what beauty and design are.

As the announcement says:

The Atlas will feature a gallery of full-color maps showcasing cartography at its most beautiful, its cleverest, its sharpest, and its most intriguing. But it will be more than a museum of images; each map will be accompanied by thoughtful commentary that guides the reader toward a deeper understanding of the work: its inspiration and message, the ways it means to influence us. It is well to look upon something beautiful and good, but once we understand how it is beautiful and good, we can carry those lessons into our own work and advance the craft of mapmaking.

Initiative for Cartographic Education

NACIS is also launching a new education program, the Initiative for Cartographic Education. The aim of ICE is to improve the quality and reach of cartography education at all levels (primary through college through professional training). As its first project, ICE will be assembling a curated database of education resources: labs, lesson plans, images, tutorials, etc. Wondering how other people teach projections? You’ll be able to look at lecture notes and slides from other educators, using them to inspire improvements in your own practice. Creating a new a lab section and need some content? Ready-to-use lab exercises will be available to help get you started. We want to make it easier for colleagues to share best practices with each other, and create an ongoing conversation about how cartography should be taught.

To do this, we need your help. If you have resources you’d be willing to share (preferably under a Creative Commons license), contact me at daniel.p.huffman@gmail.com. We can host materials, but if you already happen to have them online, we’ll also be putting URL entries into the database as well.

——

NACIS is about cartographers coming together to do great things, and both of these projects are going to be awesome. Please consider participating. And please pass this along to as many people possible. We want everyone to know what we’re up to.

Gentle readers, our first map of the new year is one that I am finally getting to eleven months after it was brought to my attention by a reader, Matthew. It concerns a favorite subject of mine, American English dialects, and was produced by hobbyist Richard Aschmann.

Click to visit Mr. Aschmann's page on North American English dialects.

The style of this work will be familiar to those with an interest in language mapping, with boundary lines delineating different pronunciations and vocabularies. Here’s another one from the Telsur Project at the University of Pennsylvania:

Click to visit Telsur project page

While Mr. Aschmann’s work is of a conventional type, it is also by far the most complex I’ve ever seen, and therein we find the problem. There is simply too much going on in this one map to be comprehensible.

One of the primary things a map reader is going to want to do is look for spatial patterns. After all, this is quite probably the entire point of having a thematic map — showing a relationship between what happens and where it happens. If you there isn’t one, then you might as make a table, instead. Now, in the case of Mr. Aschmann’s map, there’s certainly a connection between where people live and the sorts of speech patterns that come up. The problem here, though, is that this pattern is nearly impossible to discern.

To be able to see how dialects change over space requires that you look at a certain region, determine its characteristics, then look at a second region and do the same, then a third, and so on, comparing them all along the way. Your eyes sweep across the map, and each time you take a quick read and compare with what you’ve already seen. But this only works if that read can indeed be quick. With Mr. Aschmann’s map, figuring out what’s going on in any one location is a significant chore. There are so many possible symbol types, sorting through the legend is a challenge. Just figuring out which set of lines your target area falls within can be difficult, given how many layers crop up. Even if a reader is interested only in looking up data on a single place, and not making comparisons or seeing patterns, the density makes it nearly too much trouble to be worth checking. Once you’ve successfully figured out what’s going on with one region, you can move on to the next region to compare. But by the time you’ve waded through the decoding process a second time, you’ve already forgotten what the first region means. Comparison, and therefore pattern recognition, is nearly impossible, because your brain simply can’t hold that much complexity at a time or absorb it fast enough.

Compare this with a simpler map of rainfall, below. Here, it’s easy for you to quickly spot the distribution. The color pattern is simple, and you need only look for one data set, instead of twenty. There are a couple of other reasons that this map is a bit simpler to read, as well, having to do with the symbology type, but the great majority of the difference is simply in complexity.

Grabbed from Wikimedia Commons

I understand well the urge to include multiple data sets on a map, and longtime readers may recall seeing an overly complex, multivariate map of my own on this site. The more complexity you can show, the richer the story and the more versatile the product. The map quickly begins to be more than the sum of its parts. Putting two thematic layers on a map gives you three data sets — one each for the layers, plus allowing you to visualize the relationship between the two layers. One plus one equals three. But all of this is worthless if it becomes so complex as to be unclear. A map with one clear data set is worth more than a map with fifteen data sets you can’t read. Good mapmaking is about making space intelligible — otherwise, why make a map?

This map needs to be split into a series, each of which tells its portion of the story clearly. The topic it is attempting to portray is deep and rich and complex, and any single map that attempts to encompass so much is likely to end up like Mr. Aschmann’s: uselessly dense. Not every subject can be condensed into a single visual statement, and there is no shame in breaking it down into a series of simpler points in order to clarify.

Before I leave off, I’ll also mention one other thing. This map, like so many others, is going to be even less intelligible to the millions of people out there with color vision impairments. If you happen to have standard color vision and would like to see what I’m talking about, check out Color Oracle by Bernhard Jenny.

I’ve been trying of late to focus more on major items in my critiques, rather than dealing with too many nitpicky details, in order to not repeat too many points from earlier posts. Thus, I leave discussion of the rest (such as the quality of the labeling) to you, dear readers.

One Nice Thing: Mr. Aschmann has done a valiant job of trying to ensure that everything is layered clearly, which is no small task given how many data sets are crammed in. No one data set actually obscures another. There’s still far too much going on to be useful, but it’s not impossible to pull some information out of it if you’re willing to sit down and work at it.